model-intel

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name: model-intel description: Day-one intelligence gathering for newly released AI models (LLMs, generative media)

Execute comprehensive day-one intelligence gathering for: $ARGUMENTS

Scope: LLMs and generative media models only (text, image, video, audio generation models)

Timeframe: Conduct research within 8 hours of this command execution (real-time signal capture)

Output: Single consolidated intelligence report at intel/competitive/[model-name-slug]-launch-intelligence-[YYYY-MM-DD].md

Methodology Reference: frameworks/intelligence/day-one-model-intelligence-methodology.md

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Research Protocol

Phase 1: Launch 5 Parallel Research Agents (Single Message)

Execute these agents simultaneously in ONE message with multiple Task tool calls:

1. Twitter/X Vibe Agent

   • Focus: Vibe checks, first impressions, developer reactions
   • Tool: Use TWITTER_RECENT_SEARCH via Rube MCP (not WebSearch)
   • Query: "[Model Name]" OR "[model-slug]" -is:retweet with start_time set to launch day
   • Parameters: max_results: 100, sort_order: "relevancy", tweet_fields: ["created_at", "public_metrics"], expansions: ["author_id"], user_fields: ["public_metrics", "verified"]
   • Capture: High-engagement tweets with exact metrics (like_count, retweet_count, quote_count), author info (verified status, follower count), theme frequency
   • Track: Top 5-7 themes (speed, coding, reasoning, cost, multimodal, etc.)
   • Note: impression_count returns 0 for others' tweets - use like/retweet counts for engagement filtering

2. Technical Blog Agent

   • Focus: Deep dives, benchmark analysis, expert commentary
   • Sources: Artificial Analysis, Apidog, technical blogs, independent researchers
   • Capture: Benchmark validation (independent > vendor claims), architectural details, performance analysis

3. Community Forum Agent

   • Focus: Hacker News, Reddit (r/LocalLLaMA, r/MachineLearning, r/singularity)
   • Analyze: Developer technical discussions, skepticism patterns, implementation concerns
   • Note: Early activity level as market signal

4. Video/Creator Agent

   • Focus: YouTube demos, creator first looks, hands-on testing
   • Capture: What creators showcase, demo capabilities, practical observations
   • Note: May have limited day-one content (acknowledge in report)

5. Enterprise Testimonial Agent (Dedicated - Non-Blocking)

   • Focus: Company case studies, production metrics, press releases
   • Proactively search: Official launch partners, beta tester announcements, enterprise blog posts
   • Capture: Real production metrics (% improvements, cost savings, use case validation)
   • Critical: Run independently so case study hunting doesn't bottleneck main synthesis

Phase 2: Direct Searches (If Needed - Usually Covered by Agents)

Agents above should handle most research. Only use direct searches if agents miss critical sources:

• News coverage (via WebSearch if not covered)
• Reddit search (REDDIT_SEARCH if not covered by Community Forum Agent)

Note: Twitter/X search should be handled by Twitter/X Vibe Agent using TWITTER_RECENT_SEARCH, not as a separate Phase 2 step.

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Audience & Purpose

Primary readers: Product/Engineering teams conducting evals and testing pre-ship + Marketing for pulse check

Core questions to answer:

• What should we test? (Product/Eng)
• What's working? What's broken? (Product/Eng)
• What's the narrative? What's landing? (Marketing)

NOT: Strategic competitive analysis (separate doc, post-testing)

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Output Structure: VIBES + DEETS

Executive Summary

Required elements:

• Release date and positioning statement
• 3 standout capabilities (community consensus on what it's good at)
• 3 major concerns (what people are worried about)
• One-line market positioning (optional)

Max length: 3 paragraphs (150-200 words)

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THE VIBES Section

Social media reactions, community sentiment (Twitter/X, Reddit)

What People Are Hyped About:

• Top themes by frequency (with % of tweets mentioning)
• High-engagement quotes with attribution (@username, like/RT counts)
• Specific use cases and applications mentioned
• What resonates with community

What People Are Concerned About:

• Critiques and skepticism (with attribution)
• Red flags identified
• Trust/reliability concerns
• Pricing/cost trajectory worries
• Benchmark fatigue signals

Community Pulse (compress to 1 paragraph):

• HN/Reddit/forum activity with one key signal
• Overall sentiment (qualitative, not precise percentages)

Editorial rules:

• Quote with attribution and engagement metrics
• Identify themes by frequency
• Note what's surprisingly absent
• No prescriptive guidance
• Max 2-3 sentences per point
• Avoid: Media narrative analysis (not decision-critical), verbose community breakdowns, sentiment percentages

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THE DEETS Section

Technical analysis, enterprise validation, production metrics (Hacker News, blogs, enterprise)

Verified Performance Strengths:

• Benchmark performance (independent validation preferred over vendor claims)
• Enterprise production metrics (real numbers from real companies)
• Speed/latency measurements
• Architecture details (adaptive computation, thinking modes, etc.)
• Applications validated in production

Critical Technical Limitations:

• Performance trade-offs (speed vs. quality, cost vs. capability)
• Technical issues reported (bugs, gaps, migration friction)
• Feature gaps vs. competitors
• Hidden costs (token consumption patterns, pricing increases)
• Production concerns (hallucination, reliability, privacy)

Editorial rules:

• Enterprise metrics with company attribution
• Independent validation > vendor benchmarks
• Distinguish claims from validated measurements
• Technical precision
• No speculation
• Avoid: Separate "Applications in Production" subsection (redundant with testimonials), "Architecture Innovations" deep-dives (redundant with capabilities), "Platform Availability" lists (covered in Tech Specs)

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Competitive Positioning (Collapsed Toggle - "for Marketing")

Market Strategy:

• Distribution approach
• Pricing positioning
• Launch timing implications

vs. Claude / OpenAI / Open Source:

• Community perception (one paragraph each)
• Structural advantages/disadvantages

Purpose: Marketing context for model picker messaging, GTM positioning (NOT day-one engineering decision support)

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Strategic Implications (Collapsed Toggle - "for GTM Planning")

3-4 key reads (1 paragraph each):

1. Distribution/positioning strategy
2. Trust/credibility dynamics
3. Cost implications

Purpose: Post-testing strategic planning (NOT pre-ship decision support)

Editorial rule: Present strategic reads, don't prescribe action. Collapse by default in Notion - this is for later GTM work.

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Key Unknowns

5-7 questions the research can't answer yet:

• What hasn't been tested
• What needs more time
• When to revisit (7-14 days typically)

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Methodology (Collapsed Toggle)

One-line summary: "Research: 5 agents, X hrs post-launch, ~N sources. Verification: URLs verified, testimonials exact match."

Collapse by default in Notion - verification detail isn't scan-critical.

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Sources

Organized by category:

• Official (company blogs, documentation)
• News (major outlets with links)
• Technical Analysis (independent researchers, blogs)
• Community (Hacker News threads, Reddit)
• Enterprise (if found)

Format: Compact with inline links

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Anti-Redundancy Rules

Single source of truth for each fact:

• Architecture details (Adaptive Thinking, Agent Teams) → mentioned ONCE in "What People Are Hyped About," NOT repeated in separate "Architecture Innovations" section
• Financial metrics → in Enterprise Validation table, NOT separate "Financial Services Deep Dive"
• Platform availability → in Technical Specifications table, NOT separate list
• Sentiment → qualitative in "Community Pulse," NOT percentages in separate section
• Media strategy → one sentence in Community Pulse if relevant, NOT separate "Media Narrative" section

Collapse by default (for optional depth):

• Competitive Positioning → Toggle "for Marketing"
• Strategic Implications → Toggle "for GTM Planning"
• Methodology → Toggle with one-line summary
• Sources → Toggle (always at end)

What NOT to Include:

• ❌ "What to look for in your evals" sections
• ❌ "Recommended next steps" for testing
• ❌ Prescriptive recommendations on model usage
• ❌ Generic observations without signal
• ❌ Duplication between VIBES and DEETS sections
• ❌ Speculation about future capabilities

Principle: Intelligence > Advice. Diagnostic > Strategic. Report what works/breaks for testing, defer positioning analysis to collapsed sections.

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Editorial Standards

Structure:

• VIBES first (social sentiment)
• DEETS second (technical validation)
• Clear sourcing categories throughout
• No duplication between sections
• Max 2-3 sentences per paragraph

Tone:

• Direct, professional
• No AI voice patterns
• Present findings, don't editorialize
• Quote sources with attribution and engagement metrics
• Distinguish claims vs. validated measurements

Synthesis Quality:

• Consolidate similar points under single themes
• Highlight what's surprisingly absent
• Note coverage gaps honestly
• Strategic reads at end (distribution, trust, pressure, cost)

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Success Criteria

Report should enable reader to quickly understand:

1. What the community consensus says the model is good at
2. What concerns have emerged
3. How it's positioned competitively
4. What remains unknown/untested

Report should NOT:

1. Tell reader how to test or evaluate the model
2. Prescribe next steps or actions
3. Repeat information between sections
4. Include generic observations without signal

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Execute this research protocol now for the model specified above.